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A survey of deep multivariate time-series models with an empirical reproducibility audit
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DOI：10.1007/s10462-026-11674-8.png)
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Abstract
En 中文
Multivariate time series (MTS) analysis is increasingly important for extracting insights from complex, interdependent temporal data in domains such as healthcare, finance, and industrial monitoring. Recent advances in deep learning have significantly improved MTS modeling; yet, the rapidly expanding literature remains fragmented across tasks, architectures, and evaluation practices. This survey concentrates on deep learning-centric approaches in MTS research across seven key tasks: classification, clustering, forecasting, anomaly detection, imputation, representation learning, and transfer learning. To organize the field, we introduce a multi-dimensional taxonomy that categorizes methods along three complementary axes: (i) modeling and learning paradigms (e.g., Transformers, self-supervised contrastive learning, generative models, and emerging LLM-based approaches), (ii) downstream analytical objectives, and (iii) deployment-oriented capabilities such as scalability, robustness to irregular sampling and distribution shifts, interpretability, and transferability. Beyond conceptual synthesis, we conduct a targeted reproducibility audit by re-evaluating representative models using public codebases under controlled experimental conditions. Our findings show that while broad performance trends are often reproducible, exact numerical results can vary across models, datasets, and protocols, particularly when preprocessing or seed-level details are underspecified. Lastly, we identify key challenges, including high dimensionality, sparsity, non-stationarity, and concept drift, and outline seven research directions toward more scalable, reliable, and interpretable MTS systems.
Keywords:
Multivariate time series
Representation learning
Clustering
Classification
Forecasting
Anomaly detection
Imputation
Transfer learning
Downstream tasks
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